Swarms governed by NeuroSwarms self-organize into emergent, transitory
configurations in position and phase that directly recall spatial attractor
dynamics [1]}, [2]}, [3]}, [4]}, [5]} and sequential
oscillatory phenomena [6]}, [7]}, [8]}, [9]} that
have been theorized to operate within hippocampal circuits. We explicitly
designed NeuroSwarms to combine features of attractor maps and oscillatory
computing using robust transformations (such as the spatial kernels of distance
converted to synaptic strengths in equations (REF ) and (REF )). However, we did not anticipate
how readily such a system would self-organize into a variety of dynamic
spatiotemporal structures that recombined in complex patterns while supporting
navigation through our environments. A weakness of the presented implementation
was the use of a global, shared oscillation without allowing for noise, drift,
or independent perturbations [10]}, [11]}, [9]}.
A more decentralized approach might utilize resonant agent-oscillators that
self-organize local oscillations depending on available information, task
requirements, or context. Such bottom-up oscillations might aggregate into a
global, swarm-wide oscillation under certain conditions, which should be studied
in future models.
